analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from scipy.stats import chi2_contingency, ttest_ind, ttest_rel, f_oneway\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import seaborn as sns\n",
    "rc('pdf', fonttype=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contingency_table(df_review, columns=[]):\n",
    "    tab = pd.crosstab(df_review['model_attr'], df_review['user_attr'], margins=True)\n",
    "    if len(columns)>0:\n",
    "        tab = tab[columns]\n",
    "    print('contingency table')\n",
    "    display(tab)\n",
    "    tab = df_review.groupby(['model_attr', 'user_attr'])['rating'].size().unstack()\n",
    "    if len(columns)>1:\n",
    "        tab = tab[columns[:-1]]\n",
    "    g, p, dof, expctd = chi2_contingency(tab.values)\n",
    "    print('chi2', g.round(3), 'p-value', p.round(3))\n",
    "    print('')\n",
    "    print('real market size - expcted market size')\n",
    "    display((tab-expctd).round(2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chi2_test_by_year(df_review):\n",
    "    chi, p, _, expctd = chi2_contingency(df_review.groupby(['model_attr', 'user_attr'])['rating'].size().unstack().values)\n",
    "    chi1, p1, _, expctd1 = chi2_contingency(df_review.loc[\n",
    "        df_review['timestamp'].apply(lambda x: x.year<=2014)].groupby([\n",
    "        'model_attr', 'user_attr'])['rating'].size().unstack().values)\n",
    "    chi2, p2, _, expctd2 = chi2_contingency(df_review.loc[\n",
    "        df_review['timestamp'].apply(lambda x: x.year==2015)].groupby([\n",
    "        'model_attr', 'user_attr'])['rating'].size().unstack().values)\n",
    "    chi3, p3, _, expctd3 = chi2_contingency(df_review.loc[\n",
    "        df_review['timestamp'].apply(lambda x: x.year==2016)].groupby([\n",
    "        'model_attr', 'user_attr'])['rating'].size().unstack().values)\n",
    "    chi4, p4, _, expctd4 = chi2_contingency(df_review.loc[\n",
    "        df_review['timestamp'].apply(lambda x: x.year>=2017)].groupby([\n",
    "        'model_attr', 'user_attr'])['rating'].size().unstack().values)\n",
    "    df = pd.DataFrame(np.array([[chi, p, expctd.sum()],\n",
    "                                [chi1, p1, expctd1.sum()],\n",
    "                                [chi2, p2, expctd2.sum()],\n",
    "                                [chi3, p3, expctd3.sum()],\n",
    "                                [chi4, p4, expctd4.sum()],]), \n",
    "                      columns=['chi2', 'p-value', 'sum'],\n",
    "                      index=['all', '<=2014', '2015', '2016', '>=2017'])\n",
    "    display(df.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_way_anova(df_review, metric):\n",
    "    test3 = sm.stats.anova_lm(ols(metric+' ~ model_attr*user_attr', data=df_review).fit()).values[:3,-2:]\n",
    "    df = pd.DataFrame(test3, columns=['F-stat', 'p-value'], index=['product', 'user', 'product:user'])\n",
    "    display(df.round(3))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_avg_by_segment(df_review, metric, figsize, index=[], dataset='', dump=True):\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    group_rating = df_review.groupby(['user_attr', 'model_attr'])[metric].mean().unstack()\n",
    "    group_std = 1.96*df_review.groupby(['user_attr', 'model_attr'])[metric].sem().unstack()\n",
    "    if len(index)>0:\n",
    "        group_rating = group_rating.loc[index]\n",
    "    group_rating.index = group_rating.index.rename('user identity')\n",
    "    group_rating.columns = group_rating.columns.rename('product image')\n",
    "    text = []\n",
    "    for ri in range(group_rating.shape[0]):\n",
    "        row = []\n",
    "        for ci in range(group_rating.shape[1]):\n",
    "            row.append(str(group_rating.values[ri, ci].round(2))+'\\n('+r'$\\pm$'+str(group_std.values[ri, ci].round(2))+')')\n",
    "        text.append(row)\n",
    "    text = np.array(text)\n",
    "    \n",
    "    sns.heatmap(group_rating, annot=text, fmt='s', cmap=\"YlGnBu\", annot_kws={\"size\": 13},\n",
    "                cbar=False)\n",
    "    if dump:\n",
    "        fig.savefig('./plot/'+dataset+'_'+metric+'_heatmap.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ModCloth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mengting/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "dataset = 'modcloth'\n",
    "\n",
    "df_review = pd.read_csv('./data/df_'+dataset+'.csv')\n",
    "df_review['timestamp'] = pd.to_datetime(df_review['timestamp'])\n",
    "df_review['fit_score'] = 0.0\n",
    "df_review['fit_score'].loc[df_review['fit'] == 'Just right'] = 1.0\n",
    "df_review['fit_score'].loc[df_review['fit'].isna()] = None\n",
    "df_review['timestamp'] = pd.to_datetime(df_review['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Product Selection vs. Marketing Bias (ModCloth)\n",
    "### Chi2 test of contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency table\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_attr</th>\n",
       "      <th>Small</th>\n",
       "      <th>Large</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_attr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>31800</td>\n",
       "      <td>7038</td>\n",
       "      <td>38838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small&amp;Large</th>\n",
       "      <td>41361</td>\n",
       "      <td>11327</td>\n",
       "      <td>52688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>73161</td>\n",
       "      <td>18365</td>\n",
       "      <td>91526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "user_attr    Small  Large    All\n",
       "model_attr                      \n",
       "Small        31800   7038  38838\n",
       "Small&Large  41361  11327  52688\n",
       "All          73161  18365  91526"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi2 158.739 p-value 0.0\n",
      "\n",
      "real market size - expcted market size\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_attr</th>\n",
       "      <th>Small</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_attr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>754.98</td>\n",
       "      <td>-754.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small&amp;Large</th>\n",
       "      <td>-754.98</td>\n",
       "      <td>754.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "user_attr     Small   Large\n",
       "model_attr                 \n",
       "Small        754.98 -754.98\n",
       "Small&Large -754.98  754.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contingency_table(df_review, ['Small', 'Large', 'All'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>158.739</td>\n",
       "      <td>0.000</td>\n",
       "      <td>91526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;=2014</th>\n",
       "      <td>0.532</td>\n",
       "      <td>0.466</td>\n",
       "      <td>25383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>66.722</td>\n",
       "      <td>0.000</td>\n",
       "      <td>20241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>70.816</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;=2017</th>\n",
       "      <td>29.046</td>\n",
       "      <td>0.000</td>\n",
       "      <td>24663.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           chi2  p-value      sum\n",
       "all     158.739    0.000  91526.0\n",
       "<=2014    0.532    0.466  25383.0\n",
       "2015     66.722    0.000  20241.0\n",
       "2016     70.816    0.000  21239.0\n",
       ">=2017   29.046    0.000  24663.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi2_test_by_year(df_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consumer Satisfaction vs. Marketing Bias (ModCloth)\n",
    "### 2-way ANOVA on rating score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F-stat</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>171.914</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <td>46.312</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product:user</th>\n",
       "      <td>30.685</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               F-stat  p-value\n",
       "product       171.914      0.0\n",
       "user           46.312      0.0\n",
       "product:user   30.685      0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 158.4x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = two_way_anova(df_review, 'rating')\n",
    "plot_avg_by_segment(df_review, 'rating', (2.2,2), ['Small', 'Large'], dataset, dump=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-way ANOVA on clothing fit feedback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F-stat</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>293.111</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <td>402.441</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product:user</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               F-stat  p-value\n",
       "product       293.111    0.000\n",
       "user          402.441    0.000\n",
       "product:user    0.000    0.997"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 158.4x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = two_way_anova(df_review, 'fit_score')\n",
    "plot_avg_by_segment(df_review, 'fit_score', (2.2,2), ['Small', 'Large'], dataset, dump=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon Electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dataset = 'electronics'\n",
    "df_review = pd.read_csv('./data/df_'+dataset+'.csv')\n",
    "df_review['timestamp'] = pd.to_datetime(df_review['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Product Selection vs. Marketing Bias (Electronics)\n",
    "### Chi2 test of contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency table\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_attr</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_attr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>34259</td>\n",
       "      <td>31587</td>\n",
       "      <td>65846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female&amp;Male</th>\n",
       "      <td>26478</td>\n",
       "      <td>24930</td>\n",
       "      <td>51408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>25963</td>\n",
       "      <td>30907</td>\n",
       "      <td>56870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>86700</td>\n",
       "      <td>87424</td>\n",
       "      <td>174124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "user_attr    Female   Male     All\n",
       "model_attr                        \n",
       "Female        34259  31587   65846\n",
       "Female&Male   26478  24930   51408\n",
       "Male          25963  30907   56870\n",
       "All           86700  87424  174124"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi2 581.849 p-value 0.0\n",
      "\n",
      "real market size - expcted market size\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_attr</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_attr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1472.89</td>\n",
       "      <td>-1472.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female&amp;Male</th>\n",
       "      <td>880.88</td>\n",
       "      <td>-880.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>-2353.77</td>\n",
       "      <td>2353.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "user_attr     Female     Male\n",
       "model_attr                   \n",
       "Female       1472.89 -1472.89\n",
       "Female&Male   880.88  -880.88\n",
       "Male        -2353.77  2353.77"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contingency_table(df_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consumer Satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F-stat</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>62.581</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <td>3.508</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product:user</th>\n",
       "      <td>0.907</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              F-stat  p-value\n",
       "product       62.581    0.000\n",
       "user           3.508    0.061\n",
       "product:user   0.907    0.404"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 230.4x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = two_way_anova(df_review, 'rating')\n",
    "plot_avg_by_segment(df_review, 'rating', (3.2,2), [], dataset, dump=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}